tried generating cascades and recovering parameters, still got the same

result where optimization minimizes alpha, delta as much as possible

1 files changed, 307 insertions, 163 deletions

diff --git a/experiments/ml.c b/experiments/ml.c
index cdf4064..4c3f024 100644
--- a/experiments/ml.c
+++ b/experiments/ml.c
@@ -1326,7 +1326,6 @@ static char __pyx_k_range[] = "range";
 static char __pyx_k_roots[] = "roots";
 static char __pyx_k_throw[] = "throw";
 static char __pyx_k_zeros[] = "zeros";
-static char __pyx_k_arange[] = "arange";
 static char __pyx_k_import[] = "__import__";
 static char __pyx_k_thresh[] = "thresh";
 static char __pyx_k_float64[] = "float64";
@@ -1369,7 +1368,6 @@ static PyObject *__pyx_kp_s_Users_ben_Documents_Cascade_Pro;
 static PyObject *__pyx_n_s_ValueError;
 static PyObject *__pyx_n_s_age;
 static PyObject *__pyx_n_s_alpha;
-static PyObject *__pyx_n_s_arange;
 static PyObject *__pyx_n_s_args;
 static PyObject *__pyx_n_s_beta;
 static PyObject *__pyx_n_s_beta_add;
@@ -1426,9 +1424,7 @@ static PyObject *__pyx_n_s_w1;
 static PyObject *__pyx_n_s_w2;
 static PyObject *__pyx_n_s_w3;
 static PyObject *__pyx_n_s_zeros;
-static PyObject *__pyx_float__1;
-static PyObject *__pyx_float__001;
-static PyObject *__pyx_float_0_001;
+static PyObject *__pyx_float_0_13;
 static PyObject *__pyx_tuple_;
 static PyObject *__pyx_tuple__2;
 static PyObject *__pyx_tuple__3;
@@ -1501,22 +1497,22 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_success(int __pyx_v_dist, int __py
  *     cdef DTYPE_t structural, temporal, result
  *     structural = delta ** dist             # <<<<<<<<<<<<<<
  *     # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta)
- *     # temporal = exp(-alpha*dt) * (exp(alpha)-1)
+ *     temporal = exp(-alpha*dt) * (exp(alpha)-1.)
  */
   __pyx_v_structural = pow(__pyx_v_delta, ((__pyx_t_2ml_DTYPE_t)__pyx_v_dist));
 
-  /* "ml.pyx":19
+  /* "ml.pyx":18
+ *     structural = delta ** dist
  *     # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta)
- *     # temporal = exp(-alpha*dt) * (exp(alpha)-1)
- *     temporal = 1 - exp(-alpha*dt)             # <<<<<<<<<<<<<<
+ *     temporal = exp(-alpha*dt) * (exp(alpha)-1.)             # <<<<<<<<<<<<<<
+ *     # temporal = 1 - exp(-alpha*dt)
  *     if exp(-alpha*dt)==0.: print 'UNDERFLOW ERROR'
- *     # temporal = 1. / (1. + (dt - 1.)/alpha)**0.01 - 1. / (1. + dt/alpha)**0.01
  */
-  __pyx_v_temporal = (1.0 - exp(((-__pyx_v_alpha) * __pyx_v_dt)));
+  __pyx_v_temporal = (exp(((-__pyx_v_alpha) * __pyx_v_dt)) * (exp(__pyx_v_alpha) - 1.));
 
   /* "ml.pyx":20
- *     # temporal = exp(-alpha*dt) * (exp(alpha)-1)
- *     temporal = 1 - exp(-alpha*dt)
+ *     temporal = exp(-alpha*dt) * (exp(alpha)-1.)
+ *     # temporal = 1 - exp(-alpha*dt)
  *     if exp(-alpha*dt)==0.: print 'UNDERFLOW ERROR'             # <<<<<<<<<<<<<<
  *     # temporal = 1. / (1. + (dt - 1.)/alpha)**0.01 - 1. / (1. + dt/alpha)**0.01
  *     result = log(structural * temporal)
@@ -1937,8 +1933,8 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
   __pyx_t_2ml_DTYPE_t __pyx_t_23;
   __pyx_t_2ml_DTYPE_t __pyx_t_24;
   int __pyx_t_25;
-  double __pyx_t_26;
-  PyObject *(*__pyx_t_27)(PyObject *);
+  int __pyx_t_26;
+  double __pyx_t_27;
   int __pyx_t_28;
   int __pyx_lineno = 0;
   const char *__pyx_filename = NULL;
@@ -1967,7 +1963,7 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
  *         DTYPE_t beta, ll
  *         list parents, failures, successes
  *     n_roots, n_victims = len(root_victims), len(victims)             # <<<<<<<<<<<<<<
- *     n_nodes = 11270
+ *     n_nodes = 100
  *     cdef:
  */
   if (unlikely(__pyx_v_root_victims == Py_None)) {
@@ -1986,14 +1982,14 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
   /* "ml.pyx":45
  *         list parents, failures, successes
  *     n_roots, n_victims = len(root_victims), len(victims)
- *     n_nodes = 11270             # <<<<<<<<<<<<<<
+ *     n_nodes = 100             # <<<<<<<<<<<<<<
  *     cdef:
  *         np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE)
  */
-  __pyx_v_n_nodes = 11270;
+  __pyx_v_n_nodes = 100;
 
   /* "ml.pyx":47
- *     n_nodes = 11270
+ *     n_nodes = 100
  *     cdef:
  *         np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE)             # <<<<<<<<<<<<<<
  *         np.ndarray[DTYPE_t] probs_fail = np.zeros(n_victims, dtype=DTYPE)
@@ -2119,7 +2115,7 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
   __pyx_t_9 = ((PyArrayObject *)__pyx_t_3);
   {
     __Pyx_BufFmt_StackElem __pyx_stack[1];
-    if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_probs_nv.rcbuffer->pybuffer, (PyObject*)__pyx_t_9, &__Pyx_TypeInfo_nn___pyx_t_2ml_DTYPE_t, PyBUF_FORMAT| PyBUF_STRIDES, 1, 0, __pyx_stack) == -1)) {
+    if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_probs_nv.rcbuffer->pybuffer, (PyObject*)__pyx_t_9, &__Pyx_TypeInfo_nn___pyx_t_2ml_DTYPE_t, PyBUF_FORMAT| PyBUF_STRIDES| PyBUF_WRITABLE, 1, 0, __pyx_stack) == -1)) {
       __pyx_v_probs_nv = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); __pyx_pybuffernd_probs_nv.rcbuffer->pybuffer.buf = NULL;
       {__pyx_filename = __pyx_f[0]; __pyx_lineno = 49; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -2478,186 +2474,337 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
   }
   __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
 
-  /* "ml.pyx":76
+  /* "ml.pyx":65
+ * 
+ *     # loop through non-victims
+ *     for i, parents in enumerate(non_victims.itervalues()):             # <<<<<<<<<<<<<<
+ *         # for each non victim node, compute the probability that all its
+ *         # parents fail to infect it
+ */
+  __pyx_t_10 = 0;
+  __pyx_t_1 = 0;
+  if (unlikely(__pyx_v_non_victims == Py_None)) {
+    PyErr_Format(PyExc_AttributeError, "'NoneType' object has no attribute '%s'", "itervalues");
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 65; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
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+  __Pyx_XDECREF(__pyx_t_3);
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+  __pyx_t_5 = 0;
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+    if (unlikely(__pyx_t_12 == 0)) break;
+    if (unlikely(__pyx_t_12 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 65; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_5);
+    if (!(likely(PyList_CheckExact(__pyx_t_5))||((__pyx_t_5) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "list", Py_TYPE(__pyx_t_5)->tp_name), 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 65; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_XDECREF_SET(__pyx_v_parents, ((PyObject*)__pyx_t_5));
+    __pyx_t_5 = 0;
+    __pyx_v_i = __pyx_t_10;
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+    /* "ml.pyx":68
+ *         # for each non victim node, compute the probability that all its
+ *         # parents fail to infect it
+ *         failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3)             # <<<<<<<<<<<<<<
+ *                     for (dist, dt, w1, w2, w3) in parents]
+ *         probs_nv[i] = sum(failures)
+ */
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+    /* "ml.pyx":69
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+ *                     for (dist, dt, w1, w2, w3) in parents]             # <<<<<<<<<<<<<<
+ *         probs_nv[i] = sum(failures)
+ * 
+ */
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+      PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
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+ *                     for (dist, dt, w1, w2, w3) in parents]             # <<<<<<<<<<<<<<
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+ * 
+ */
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+ *         probs_nv[i] = sum(failures)             # <<<<<<<<<<<<<<
+ * 
+ *     # print successes
+ */
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  *     # iterate over all victim nodes to find the optimal threshold
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+    __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_sum); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 92; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -2671,56 +2818,56 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
       }
     }
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-      __pyx_t_4 = __Pyx_PyObject_CallOneArg(__pyx_t_6, __pyx_t_3); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      __pyx_t_4 = __Pyx_PyObject_CallNoArg(__pyx_t_6); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_4 = __Pyx_PyObject_CallNoArg(__pyx_t_6); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 92; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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     __Pyx_GOTREF(__pyx_t_4);
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     __Pyx_XDECREF_SET(__pyx_v_beta_add, __pyx_t_4);
     __pyx_t_4 = 0;
 
-    /* "ml.pyx":90
+    /* "ml.pyx":94
  *         beta_add = (probs[probs>=thresh]).sum()
  *         # add probability for the seeds and non-seeds
- *         beta_add += roots * log(beta) + (n_nodes-roots) * log(1 - beta)             # <<<<<<<<<<<<<<
+ *         beta_add += roots * log(beta) + (n_nodes-roots) * log(1. - beta)             # <<<<<<<<<<<<<<
  * 
  *         if beta_add > max_beta_add:
  */
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+    __pyx_t_4 = PyFloat_FromDouble(((__pyx_v_roots * log(__pyx_v_beta)) + ((__pyx_v_n_nodes - __pyx_v_roots) * log((1. - __pyx_v_beta))))); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 94; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    __pyx_t_6 = PyNumber_InPlaceAdd(__pyx_v_beta_add, __pyx_t_4); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 90; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_6 = PyNumber_InPlaceAdd(__pyx_v_beta_add, __pyx_t_4); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 94; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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     __Pyx_DECREF_SET(__pyx_v_beta_add, __pyx_t_6);
     __pyx_t_6 = 0;
 
-    /* "ml.pyx":92
- *         beta_add += roots * log(beta) + (n_nodes-roots) * log(1 - beta)
+    /* "ml.pyx":96
+ *         beta_add += roots * log(beta) + (n_nodes-roots) * log(1. - beta)
  * 
  *         if beta_add > max_beta_add:             # <<<<<<<<<<<<<<
  *             max_beta = beta
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+    __pyx_t_6 = PyObject_RichCompare(__pyx_v_beta_add, __pyx_v_max_beta_add, Py_GT); __Pyx_XGOTREF(__pyx_t_6); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 96; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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     __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
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-      /* "ml.pyx":93
+      /* "ml.pyx":97
  * 
  *         if beta_add > max_beta_add:
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  *             max_beta_add = beta_add
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+      __pyx_t_6 = PyFloat_FromDouble(__pyx_v_beta); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 97; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      /* "ml.pyx":94
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-      /* "ml.pyx":95
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+      goto __pyx_L21;
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-    /* "ml.pyx":82
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-  /* "ml.pyx":98
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-  /* "ml.pyx":99
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-  /* "ml.pyx":100
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-  /* "ml.pyx":82
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  *     # iterate over all victim nodes to find the optimal threshold
- *     for beta in np.arange(0.001, .1, .001):             # <<<<<<<<<<<<<<
+ *     for beta in [0.13]:#np.arange(0.001, .2, .002):             # <<<<<<<<<<<<<<
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